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Leveraging Knowledge Graph Embeddings for Natural Language Question Answering

: Wang, R.J.; Wang, M.; Liu, J.; Chen, W.T.; Cochez, M.; Decker, S.


Li, G.:
Database Systems for Advanced Applications : 24th International Conference, DASFAA 2019. Part 1 : Chiang Mai, Thailand, April 22–25, 2019. Proceedings
Cham: Springer International Publishing, 2019 (Lecture Notes in Computer Science 11446)
ISBN: 978-3-030-18576-3
ISBN: 978-3-030-18575-6
International Conference on Database Systems for Advanced Applications (DASFAA) <24, 2019, Chiang Mai/Thailand>
Conference Paper
Fraunhofer FIT ()

A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.